EFFECTS OF WORKING CAPITAL MANAGEMENT ON FINANCIAL PERFORMANCE OF PRIVATE MANUFACTURING FIRMS IN KENYA EMMAH WAMBURA GAKONDI A RESEARCH PROJECT SUBMITTED TO THE DEPARTMENT OF BUSINESS ADMINISTRATION IN THE SCHOOL OF BUSINESS ADMINISTRATION IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF THE DEGREE OF MASTER IN BUSINESS ADMINISTRATION AT JOMO KENYATTA UNIVERSITY OF AGRICULTURE AND TECHNOLOGY 2018DECLARATIONThis Research Project is my original work and has not been submitted for a degree in any other University Signature

EFFECTS OF WORKING CAPITAL MANAGEMENT ON FINANCIAL PERFORMANCE OF PRIVATE MANUFACTURING FIRMS IN KENYA
EMMAH WAMBURA GAKONDI
A RESEARCH PROJECT SUBMITTED TO THE DEPARTMENT OF BUSINESS ADMINISTRATION IN THE SCHOOL OF BUSINESS ADMINISTRATION IN PARTIAL FULFILLMENT OF THE REQUIREMENT FOR THE AWARD OF THE DEGREE OF MASTER IN BUSINESS ADMINISTRATION AT JOMO KENYATTA UNIVERSITY OF AGRICULTURE AND TECHNOLOGY
2018DECLARATIONThis Research Project is my original work and has not been submitted for a degree in any other University
Signature: …………………………… Date: …………………….

Gakondi Emmah Wambura
HD 333-3192/2014
This is to certify that this research project has been submitted for examination with my approval as the University Supervisor
Signature: ………………………………….. Date: …………………………..

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Prof. Willy Muturi
Jomo Kenyatta University of Agriculture and Technology-Kenya
ACKNOWLEDGEMENTI express my gratitude to my supervisor Prof. Willy Muturi for providing valuable guidance during the research project period. His constructive suggestions and right criticisms helped me stay on course. I am also deeply indebted to my parents for their contribution and support throughout this work. I finally give thanks to the Almighty God for granting me great guidance, energy, wisdom and finances which enabled me accomplish this work.

TABLE OF CONTENTSTable of Contents
TOC o “1-3” h z u DECLARATION PAGEREF _Toc527215300 h iiACKNOWLEDGEMENT PAGEREF _Toc527215301 h iiiTABLE OF CONTENTS PAGEREF _Toc527215302 h ivLIST OF TABLES PAGEREF _Toc527215303 h viiLIST OF FIGURES PAGEREF _Toc527215304 h viiiACRONYMS PAGEREF _Toc527215305 h ixABSTRACT PAGEREF _Toc527215306 h xCHAPTER 1 PAGEREF _Toc527215307 h 1INTRODUCTION PAGEREF _Toc527215308 h 11.1Background of the Study PAGEREF _Toc527215309 h 11.2 Problem Statement PAGEREF _Toc527215310 h 61.3 Objectives of the Study PAGEREF _Toc527215311 h 81.4 Research Questions PAGEREF _Toc527215312 h 81.5 Justification PAGEREF _Toc527215313 h 91.6 Scope PAGEREF _Toc527215314 h 9CHAPTER TWO PAGEREF _Toc527215315 h 10LITERATURE REVIEW PAGEREF _Toc527215316 h 102.1 Introduction PAGEREF _Toc527215317 h 102.2 Working Capital Theories PAGEREF _Toc527215318 h 102.2.1 The Agency Theory PAGEREF _Toc527215319 h 102.2.2 The Trade Off Theory PAGEREF _Toc527215320 h 112.2.3 Asset Profitability Theory. PAGEREF _Toc527215321 h 122.2.4 Cash Management Theory PAGEREF _Toc527215322 h 132.3 Conceptual Framework PAGEREF _Toc527215323 h 142.4 Literature Review PAGEREF _Toc527215324 h 162.4.1 Accounts Receivables PAGEREF _Toc527215325 h 162.4.2 Cash Management PAGEREF _Toc527215326 h 172.4.3 Accounts Payables PAGEREF _Toc527215327 h 192.4.4 Inventories conversion PAGEREF _Toc527215328 h 212.5 Critique of the existing literature PAGEREF _Toc527215329 h 232.6 Summary PAGEREF _Toc527215330 h 242.7 Research Gaps PAGEREF _Toc527215331 h 24CHAPTER THREE PAGEREF _Toc527215332 h 27METHODOLOGY PAGEREF _Toc527215333 h 273.0 Introduction PAGEREF _Toc527215334 h 273.1 Research Design PAGEREF _Toc527215335 h 273.2Population PAGEREF _Toc527215336 h 273.3 Sampling Frame PAGEREF _Toc527215337 h 283.4 Sample Size and Sampling Technique PAGEREF _Toc527215338 h 283.5 Data collection Instruments PAGEREF _Toc527215339 h 293.6 Data Processing and Analysis PAGEREF _Toc527215340 h 293.6.1 Empirical Model PAGEREF _Toc527215341 h 303.7 Operationalization of Variable PAGEREF _Toc527215342 h 30CHAPTER FOUR PAGEREF _Toc527215343 h 32RESULTS AND DISCUSSIONS PAGEREF _Toc527215344 h 324.1 Introduction PAGEREF _Toc527215345 h 324.2 Descriptive statistics PAGEREF _Toc527215346 h 324.2.1 Accounts receivables PAGEREF _Toc527215347 h 324.2.2 Cash management PAGEREF _Toc527215348 h 334.2.3 Account payable PAGEREF _Toc527215349 h 334.2.4 Inventory conversion PAGEREF _Toc527215350 h 334.2.5 Financial performance PAGEREF _Toc527215351 h 344.3 Unit root test PAGEREF _Toc527215352 h 344.3.1 Financial Performance PAGEREF _Toc527215353 h 344.3.2 Account Receivable PAGEREF _Toc527215354 h 354.3.3 Cash Management PAGEREF _Toc527215355 h 364.3.4 Account Payable PAGEREF _Toc527215356 h 364.3.5 Inventory Management PAGEREF _Toc527215357 h 374.4 Correlation Analysis PAGEREF _Toc527215358 h 384.5 Cointegration test PAGEREF _Toc527215359 h 394.6 Heteroskedasticity PAGEREF _Toc527215360 h 394.7 Autocorrelation PAGEREF _Toc527215361 h 394.8 Variance inflated factor PAGEREF _Toc527215362 h 404.9 Regression results PAGEREF _Toc527215363 h 404.9.1 Accounts receivables PAGEREF _Toc527215364 h 414.9.2 Cash management PAGEREF _Toc527215365 h 424.9.3 Account payable PAGEREF _Toc527215366 h 424.9.4 Inventory conversion PAGEREF _Toc527215367 h 43CHAPTER FIVE PAGEREF _Toc527215368 h 45SUMMARY, CONCLUSIONS AND RECOMMENDATIONS PAGEREF _Toc527215369 h 455.1 Introduction PAGEREF _Toc527215370 h 455.2 Summary of findings PAGEREF _Toc527215371 h 455.2.2 Effect of Accounts receivables on financial performance PAGEREF _Toc527215372 h 455.2.3 Effect of Cash management on financial performance PAGEREF _Toc527215373 h 455.2.4 Effect of Account payable on financial performance PAGEREF _Toc527215374 h 465.2.5 Effect of Inventory conversion on financial performance PAGEREF _Toc527215375 h 465.3 Conclusion PAGEREF _Toc527215376 h 465.4 Recommendation PAGEREF _Toc527215377 h 475.4.1 Account payable variable PAGEREF _Toc527215378 h 485.4.2 Inventory conversion PAGEREF _Toc527215379 h 485.4.3 Cash management PAGEREF _Toc527215380 h 485.4.4 Accounts receivables PAGEREF _Toc527215381 h 485.5 Area for further research PAGEREF _Toc527215382 h 49REFERENCES PAGEREF _Toc527215383 h 50
List of Tables TOC h z “Caption” c Table 4.1 Descriptive statistics PAGEREF _Toc527204479 h 32Table 4.2 Financial Performance PAGEREF _Toc527204480 h 34Table 4.3 Account Receivable PAGEREF _Toc527204481 h 35Table 4.4 Cash Management PAGEREF _Toc527204482 h 36Table 4.5 Account Payable PAGEREF _Toc527204483 h 36Table 4.6 Inventory Management PAGEREF _Toc527204484 h 37Table 4.7: Correlation Analysis PAGEREF _Toc527204485 h 38Table 4.8: Kao Residual Cointegration Test PAGEREF _Toc527204486 h 39Table 4.9: Heteroskedasticity PAGEREF _Toc527204487 h 39Table 4.10: Autocorrelation PAGEREF _Toc527204488 h 40Table 4.11: Variance inflated factor PAGEREF _Toc527204489 h 40Table 4.12: Regression Coefficients PAGEREF _Toc527204490 h 41List of Figures TOC h z “Caption” c Figure 2.1: Conceptual framework PAGEREF _Toc527215240 h 15

ACRONYMSANOVA: Analysis of Variance
CCC: Cash Conversion Cycle
DSE: Dar es Salaam Stock Exchange
GDP: Gross Domestic Product
KAM: Kenya Association of Manufacturers
NSE: Nairobi Stock Exchange
OECD: Organization for Economic Cooperation and Development
ROA: Return on Assets
ROE: Return on Equity
ROI: Return on Investment
SME: Small and Medium Enterprises
SPSS: Statistical Package for Social Sciences
UNIDO: United Nations Industrial Development Organization
WCM: Working Capital Management
ABSTRACTIn Kenya, manufacturing is the second most important sector, the first being Agriculture. The sector’s importance is based on its contribution to gross domestic product, foreign exchange earnings and the number of people it employs. The sector is however struggling since the 1980s and has seen some companies cease operating. Unfavorable working conditions have been cited as one of the huge causes why the companies have ceased operation. This issue has causes manufacturing firms to maintain inadequate or excessive working capital levels, of which both scenarios are undesirable and impact negatively on the financial performance. Therefore, the objective of this research was to determine the effect of working capital on financial performance of manufacturing firms in Kenya. The study had four objectives, which are, to establish the impact of accounts receivables on financial performance of manufacturing firms in Kenya, examine the effect of accounts payables levels of financial performance of manufacturing firms in Kenya, establish the impact of cash on financial performance of manufacturing firms in Kenya and lastly to establish the impact inventory levels and management have on financial performance of private manufacturing firms in Kenya. The study employed a multi correlational research design. Secondary data was used and a record survey sheet was used to collect. The target was 311 private manufacturing firms that are registered by KAM and which are located in Nairobi Indusstrial Area and its environments. Proportional allocation was used to determine the size of each sample for different strata. The data collected was analyzed using Eviews statistical software for analysis. The general multiple regression analysis was then used to estimate causal relationship between financial performance and the independent variables. The results of the study showed that Accounts payables and Account Receivables have a positive and statistically significant effect on Financial Performance of private manufacturing firms in Kenya. Cash and Inventory were found to be important variables in the determination of financial performance and the implication was that with these resources the firm amassed more resources to continue with its operations and consequently realized increases in financial performance. The model was tested using the F-test at a significance level of 5%. The findings of the research showed that the independent variables had a significant combined effect (R squared = 0.584223) on Financial performance of manufacturing firms in Kenya. The study makes the following recommendations; private manufacturing firms should pay their suppliers early to enjoy good working relationship with them and to enjoy the discounts offered by creditors for early payments made, firms should put in place modern inventory control systems and establish optimal cash levels. Board of Directors and Finacial managers are recommended to come up with ideal credit policies that minimize the risks of bad debts and collection costs.

CHAPTER 1INTRODUCTION1.1Background of the StudyWorking capital deals with the financial stability of a company and also plays a crucial role in maximizing shareholders wealth, therefore a very important aspect in every company. Working capital refers to the capital available for running the day to day operations of the business and consists of current assets and current liabilities. Working capital is among the top on the list of firms challenges currently being faced by manufacturing firms. In fact, the only issue ranked as a bigger concern is escalating costs- which are also directly related to working capital and cashflow. Maintaining an optimum level of working capital is difficult. A firm should have neither excess working capital nor inadequate working capital because both scenarios have adverse effects on profitability and liquidity positions. It is therefore, important that management puts in place effective working capital management practices. Efficiency in working capital management makes it possible for the firm to maximize the benefits from net current assets by having an optimum level to meet working capital demands. In manufacturing firms, administration of working capital is an important and challenging task due to the high proportion of working capital involved and its peculiar characteristics.

Liquidity and profitability are two important aspects of corporate business life (Vataliya, 2009). The challenge is that increasing profits at the cost of liquidity can result in serious problems for the firm. As a result, there must be a tradeoff between the liquidity and profitability objectives in a firm. Each one of them has its own importance, and none of them should be sacrificed at the cost of the other. If firms do not make profits they cannot survive in the long term, while on the other hand if they are not liquid they may face the threat of insolvency or bankruptcy. Managers must therefore put into consideration working capital management because they ultimately affect the profitability of firms. With efficient and effective management of working capital, firms can achieve maximum profitability and still maintain adequate liquidity.

Inefficient financial management which includes working capital management can damage a firm’s profitability (Gebrehiwot and Wolday, 2006). Efficient working capital management results in improved operating performance of the business concern and helps in meeting short term liquidity needs (Paramasivan and Subramanian,2009).Further, effective management of working capital is a fundamental in the overall corporate strategy to create shareholders value (Nazir and Afza,2008). It’s therefore important that firms keep an optimal level of working capital that maximizes their value (Deloof, 2003). Additionally, effective working capital management is important because it affects the performance and liquidity of firms (Taleb et al, 2010).

Huge inventory base and flexible trade credit policy may result in huge sales. Large inventory also minimizes the risk of a stock out. Trade credit stimulates sales because clients access product quality before paying (Raheman and Nasr, 2007). Also according to the same researchers delaying payment of accounts payables to suppliers offers firms the opportunity to access the quality of products and it is an inexpensive and flexible source of financing. On the other hand, delaying accounts payables may turn out to be expensive if a firm is offered a discount for early payment. Uncollected accounts receivables may also result in cash inflow problems for the firm.

Cash conversion cycle is one popular measure of working capital management, it is the time span between the expenditure for the purchases of raw materials and the collection of sales of finished goods. The longer the time lag, the larger the investment in working capital, and again a long cash conversion cycle may result in increased profitability because it results in higher sales (Deloof, 2003). However, corporate profitability may decrease with the cash conversion cycle, if the cost of higher investment in working capital rise faster than the benefits of holding more inventories or granting more trade credit to customers.

The main cause of business enterprise failure has been found to be the shortage of working capital, their mishandling and mismanagement of working capital and underutilization of capacity (Vataliya, 2009). Several researchers have identified the impact of working capital management of performance of organizations, but no significant work has been carried out on the impact of working capital management on the performance of private manufacturing firms in emerging economies like Kenya. This limited evidence in the Kenyan context together with the importance of working capital management calls for research on their impact on firm’s performance.

Global perspective
Countries in the west, particularly countries under Organization for economic co-operation and development (OECD), are going through a declining trend in the manufacturing sector. In recent years, in most OECD countries manufacturing employment has declined steadily and so has the output (OECD, 2006). In the United States, manufacturing employment as measured by the survey of workers fell by 9% from 2008 through 2014. Similar decline was experienced in Canada, France, Italy, United Kingdom, Japan and Sweden over the same period (Levinson, 2016).Between the periods from 1990 to 2014, manufacturing employment fell by a lower percentage in the United States than in United Kingdom and by almost the same percentage in France, Japan and Sweden. Between 2004 and 2008 more than one in every seven manufacturing jobs (322,000) were lost in Canada. Other high income countries including Italy, Netherlands and Germany experienced large declines in manufacturing employment over that period (Levinson, 2016).
The manufacturing sector in the developed economies is large and contributes greatly to the economic development. While the manufacturing sector has been declining for the last two decades, it remains a vital part of developed countries economies. In 2013, the sector employed about 8.8% of total U.S people in employment which is about twelve million workers (MEP, 2016). In 2009 the sector employed about 2.6 million people in the U.K which is over 8% of total UK employment. The sector also generated one hundred billion pounds in gross value added which represents 11% of the UK economy (BIS, 2010).

Regional perspective
According to 2012 report by World Bank, manufacturing accounts for only 13% of GDP in Sub Saharan Africa. Manufacturing accounts for 25% of exports in SSA (World Bank, 2012).In Nigeria, the manufacturing sector is small accounting for an estimated 2.6%GDP in 2012. Real growth in the sector averaged 8.5 p.a during the period of 2005-2012. Manufacturing capacity utilization is also low, averaging slightly above 57% in 2011- 2012 (KPMG, n.d). In South Africa, the sector accounts for an average of 17.4% of its GDP, 9% employment and 40% of its total exports (Republic ofNamibia,2007). The sector however faces operational and profitability challenges. In Morocco, the manufacturing industry is an important sector of the Moroccan economy and accounts for 15%-16% of GDP and is also a major source of export earnings (KPMG, n.d). The manufacturing sector is also one of the most important components of the Tunisian economy, with manufacturing exports accounting for 70% of the total exports (KPMG, n.d).

Local Perspective
Kenya’s manufacturing sector is relatively strong compared to other countries that are in a similar phase of economic development. In 2013, the sector contributed an estimated 10.6% to GDP. Kenya is also one of the top exporters of manufactured goods in SSA region. According to the United Nations Industrial Development Organization (UNIDO), Kenya’s manufacturing value added per capita was US$ 61.8 in 2012, up 2.6 % in real terms from 2005. Kenya’s manufacturing sector is dominated by food and consumer goods processing, meat and fruit canning, wheat flour and maize milling and sugar refining. Real growth in the manufacturing sector averaged 4.1 % p.a. during 2006- 2013, lower than the average annual growth in overall real GDP of 4.6% p.a. (KPMG, n.d).Consequently, manufacturing sector output has declined in recent years which exposes a gap in the country’s ability to achieve a fully industrialized economy by 2030, in accordance with the country’s vision 2030.

Manufacturing sector employs about 20% of the total workers in the economy, which is higher than what other sectors employ. This asserts that the manufacturing sector is an important sector in the Kenyan economy, and developing this sector will generate more employment, foreign exchange and increased gross domestic product. In 2012, the total number of workers employed in formal, informal, private and public sectors stood at 2,105,000 against the total workers population of 11,399,800b(Kenya, Republic of, 2013).There was a total increase of workers employed in the manufacturing sector between 2008 and 2012 by an estimated 273,100 workers as shown in the fig below.

Table 1.1: Total Number of Workers in Manufacturing Sector between 2008 ; 2012
Category 2008 2009 2010 2011
2012
Private sector 237,900 237,200 238,600 242,400 247,600
Public sector 26,900 26,900 27,800 27,900 28,100
Informal sector 1,567,100 1,644,200 1,711,200 1,780,800 1,829,300
Total manufacturing 1,831,900 1,908,300 1,977,600 2,051,100 2,105,000
Total economy 9,411,400 9,886,400 10,389,000 10,885,300 11,399,800
Source: Kenya, Republic of (2013)
There are 721 manufacturing firms registered in the directory of Kenya Association of manufacturers (KAM,2016). KAM is a membership organization whose role is to provide leadership and services aimed at enhancing the development of a competitive manufacturing sector in Kenya. Manufacturing firms registered under KAM are more formal than other unregistered firms which makes this sector an appropriate area of study, especially because the study requires sensitive financial information. The target population will be 311 private firms that are located in Nairobi and its environs that are in the 2017 directory of KAM.

1.2 Problem StatementThe manufacturing industry in Kenya accounts for about 14% of the Gross Domestic Product, which is a slight increase since independence because the sector has stagnated since the 1980’s. In Kenya a lot of manufacturing firms are struggling to break even and some key players have been forced to relocate to other countries. Cadbury East Africa, Colgate Palmolive, Reckitt Benckiser and Procter and Gamble are some of the huge international firms that have stopped their manufacturing operations in Kenya, preferring to base their manufacturing in other countries. Eveready, Tata Chemicals, Kenya Fluorspar are some of the many firms that have scaled down their operations by closing some of their factories. Sameer Africa has also closed down its Yana tyres manufacturing factory in Nairobi after being in operation for decades.

According to Kenya Manufacturers’ Association about 10 to 15 companies shut down every year. Majority of these firms are privately owned. Companies are shutting down and others are operating at breakeven point (KAM, 2006). All these companies cite high operation costs as the major cause of the precarious financial situation (Republic of Kenya, 2007). If these problems are not addressed manufacturing firms can go under which can have a significant ripple effect on the entire economy (Ali, 2009). This will also mean that Kenya will not be able to achieve its vision 2030 of being a middle level economy.

Most of the past researchers have found a significant relationship between working capital management and firms’ financial performance. The researches have also discovered that some criteria used by managers in making working capital decisions do not rely on Finance principles, but rather they use vague rule of thumb or poorly constructed models (Emery, Finnerty and Stowe, 2004). This results in inefficient use of working capital, which results in an organization being overcapitalized, undercapitalized or liquidating. According to Egbide(2009) large number of business failures is largely due to the inability of the financial manager to plan and control the working capital of their firms. These working capital practices inadequacies are still practiced today in the form of high bad debts, high inventory costs, liquidity problems etc. that adversely affect their operating performance (Egbide, 2009).

In spite of the above consequences, this field has not been given significant attention in Kenya. After carrying out literature review, the researcher failed to find any directly related research topics carried out in Kenya, particularly in private manufacturing firms which makes the largest number compared to public manufacturing firms. The researcher therefore, believes the problem is largely untouched and there is a knowledge gap that needs to be filled.
Lack of proper research on this area presents Kenyan company managers with limited facts in relation to working capital management and its effect on firm’s performance. This constitutes the study problem, and thus the need to study effect of working capital practices on the performance of private manufacturing firms in Kenya, Nairobi in particular.

1.3 Objectives of the StudyGeneral Objective
To determine the effect of working capital management on financial performance of private manufacturing firms in Kenya
Specific Objectives
To determine the effect of accounts receivables on financial performance of private manufacturing firms in Kenya.

To determine the effect of accounts payables on financial performance of private manufacturing firms in Kenya
To determine the effect of inventory control on financial performance of private manufacturing firms in Kenya
To determine the effect of cash management on financial performance of private manufacturing firms in Kenya
1.4 Research Questions1 Accounts receivables has no effect on the financial performance of private manufacturing firms in Kenya.

2 Account payables has no effect on the financial performance of private manufacturing firms in Kenya.
3 Inventory control has no effect on the financial performance of private of manufacturing firms in Kenya.

4 Cash management has no effect on the financial performance of private manufacturing firms in Kenya.

1.5 JustificationThe research findings of this study will be utilized by Finance managers of manufacturing firms in designing working capital management reform models. The research findings will make it possible to understand advantages and disadvantages of techniques of working capital management components in manufacturing industries. The research study will illustrate how essential working capital management strategies are for the manufacturing firms.

The findings of this study will be used by Kenya Association of Manufacturers (KAM) to advise the firms that they oversee. The research will provide evidence of the correlation between working capital and profitability that KAM and all the firms under them will find helpful.

Scholars and researchers shall find this research quite of interest because of the gaps for further research that shall be recommended at the end of the study.

1.6 ScopeThis study will focus on private manufacturing firms located in Kenya exclusively. The firms will be narrowed to only those registered with the Kenya Association of Manufacturers (KAM). The target population will be private manufacturing firms registered with KAM located in Nairobi industrial area and its environs.

CHAPTER TWOLITERATURE REVIEW2.1 IntroductionThis chapter reviews the literature available on working capital. It summarizes information from past researches carried out on the field of working capital and its effect on firm’s financial performance. The specific areas covered in this chapter are; theoretical literature, the conceptual framework, empirical literature review and the assessment of the gaps to be filled by the study.

2.2 Working Capital TheoriesThese are the various theories that support the significance of working capital. Some of the most important theories pertinent to working capital management include the following:
2.2.1 The Agency TheoryThe agency relationship is where one or more persons referred to as the principal is engaged by another called the agent to perform tasks or services on their behalf (Jensen and Mecking, 1976). The theory’s focal point is looking at how to ensure agents (managers and executives) act in the best interest of the principals (shareholders and owners) of an organization. The term stakeholders refer to groups of constituents who have a legitimate claim on the firm (Freeman, 1984). Stakeholders include managers, creditors, shareholders, suppliers, employers and the general public.

The relevance of agency theory to WCM could be viewed from the perspective of the financial management who is the agent of the owners (principals) of the firm, and who takes all important decisions regarding all the short term assets and liabilities of the firm. He takes charge of decisions regarding receivables, payables, inventories /stock and liabilities of a firm. However, by extending this to stakeholder relevance, as highlighted earlier, the symbiotic association of firm and various stakeholders, the creditors for instance, provides source of finance to the firm and in exchange expects repayment of their loans on schedule. The stockholders supply the firm`s capital and in return expects a maximized risk-adjusted return from their investment. Employees and manager help firms with required skills, time, as well as human capital requirements in exchange they anticipate good working condition, fair income and remunerations. Customers provide the source of revenue to the firms and in exchange expect to have value for money and satisfactory services. Suppliers are input providers to the firm, and hence expect fair prices and dependable buyers. Stakeholders normally differ with respect to their stake size in firms. The level of individual`s stake depends on the extent of his exchange of relationship and commitments with the firm which is based on specific asset investments (Williamson, 1984).

2.2.2 The Trade Off TheoryEvery decision with respect to investment is based on risk and return relationship (Richard, Stewart ; Franklin, 2008). The two conflicting attitudes are always associated with risk, which is the risk seeking behaviour and the risk aversion. The risk seekers prefer choices involving greater probability of loss with a strong tendency of over estimating gains. Risk seekers main focus is on the opportunities for gain (Tiegen ; Brun, 1997). Risk averters on the other hand tend to overestimate losses and underestimate the gains.

In order to integrate the trade off theory in working capital it’s important to stress that one of the cardinal decisions in working capital management is the tradeoff between liquidity and profitability. When a firm chooses liquidity it’s at the expense of profitability and vice versa. The two conflicting decisions may result in either excess or shortage of the components of working capital and the current assets of a business. Excess of cash is considered non-earning and this reduces profitability. Shortage of cash on the other hand causes crisis in liquidity which results in inability to make payments, disruption of operations and ultimately affects profit. Excess in receivables is associated with collection effort costs, risk associated with defaults and low profit. Shortage of receivables on the other hand results in low turnover hence low profitability. Excess in inventory results in price decline, associated with carrying costs, opportunity cost of funds which affects profits adversely. Shortage in inventory results in limited supplies tends to interrupt production schedules, lower sales and profits.
The trade off theory is associated with working capital when looking inwardly at the ability of a firm or financial manager to determine the collection of assets, or the portfolio to be acquired, decisions on what composition of receivables, inventories, incentives and the profitability concern.

2.2.3 Asset Profitability Theory.Asset profitability theory by Sathamoorthi (2002) states that increase in current asset to total assets ratio has a negative effect on firms’ profitability, while on the other hand, increase in current liabilities to total liabilities ratios has a positive effect on profitability of firms. This theory notes that decrease in current asset to total assets ratio as well as increase in the ratio of current liabilities to total liabilities ratios, when considered independently, lead to an increased profitability coupled with a corresponding increase in risk. Increase in the ratio of current assets to total assets decline in profitability because it is assumed that (i) current assets are less profitable than fixed assets; and (ii) short-term funds are less expensive than long-term funds. Decrease in the ratio of current assets to total assets will result in an increase in profitability as well as risk. The increase in profitability will primarily be due to the corresponding increase in fixed assets which are likely to generate higher returns because corresponding increase in fixed assets which are likely to generate higher returns (Sathamoorthi, 2002).
On the other hand, Sathamoorthi (2002) points that effect of an increase in the ratio of current liabilities to total assets would be that profitability will increase. The reason for the increased profitability lies in the fact that current liabilities, which are a short-term source of finance, will increase, whereas the long-term sources of finance will be reduced. As short-term sources of finance are less expensive than long-run sources, increase in the ratio will mean substituting less expensive sources for more expensive sources of financing. There will therefore be a decline in cost and a corresponding rise in profitability.
2.2.4 Cash Management TheoryCash management Theory is concerned with the managing of cash flows into and out of the firm; cash flows within the firm and cash balances held by the firm at a point of time by financing deficit or investment surplus cash. Short term management of corporate cash balances is a major concern of every firm because it’s impossible to predict cash flows accurately, more so the inflows, and there is no perfect coincidence between cash outflows and inflows. During some periods cash out flows will exceed cash inflows due to payments for dividends, tax or inventory build up. At other times, cash inflow will be more than cash sales and debtors may realize in large amounts promptly (Pandey, 2005). An imbalance between cash inflows and outflows would mean failure of cash management function of the firm. Persistence of such an imbalance may cause financial distress to the firm and, hence, business failure (Aziz ; Dar, 2008).

The theory helps the present day companies to manage their cash while taking into consideration the fluctuations in daily cash flow. As per the theory companies let their cash balance move within two limits – the upper limit and the lower limit. The companies buy or sell the marketable securities only if the cash balance is equal to any one of these. 
When the cash balances of a company touches the upper limit it purchases a certain number of saleable securities that helps them to come back to the desired level. If the cash balance of the company reaches the lower level then the company trades its saleable securities and gathers enough cash to fix the problem. 
2.3 Conceptual FrameworkIn order to hold new and existing knowledge, theory should provide a conceptual framework, in order for knowledge to be interpreted for application in a comprehensive manner. In this study the conceptual framework comprises of four independent variables and one dependent variable.

-12382579375Accounts receivables
Account receivablesalesX 365
Cash management
(Average collection period+ inventory turnover)-average (payment period)
Accounts payables
Account payablecost of goods soldX 365
Inventories
invetory cost of goods soldX 365
Financial Performance
Net incomeTotal assetsX100%
00Accounts receivables
Account receivablesalesX 365
Cash management
(Average collection period+ inventory turnover)-average (payment period)
Accounts payables
Account payablecost of goods soldX 365
Inventories
invetory cost of goods soldX 365
Financial Performance
Net incomeTotal assetsX100%

323786515938500
Figure 2.1: Conceptual frameworkIndependent variablesDependent variable
2.4 Literature Review2.4.1 Accounts ReceivablesAccounts receivables are also known as debtors and occur when a company gives their customers credit terms to pay for products or services. Customers that buy products or services on credit are called sundry debtors and are a major component in business. A study conducted by Bougheeas, Mateut and Mizen (2009) showed that in Germany and Italy account receivables account for more than a quarter of their total assets. In American firms, 17.8% of the total assets are account receivables (Rajan and Zingales, 1995). This makes management of accounts receivables indispensable. There are several reasons for extending trade credit to consumers.

Deloof (2003) undertook a study of 1009 large non financial firms for a period of five years from 1992- 1996 to determine if working capital affects profitability of Belgian firms. In his study, Deloof (2003) measured profitability by gross operating income which he calculated as (Total Assets minus Financial Assets). He justified his deducting of financial assets from total Assets in the formula by asserting that a number of firms in his sample, financial assets, which mainly comprise shares in other firms, are a significant part of the total assets and as such operating activities would have contributed little to the overall ROI. By using correlation and regression analysis, Deloof found a significant negative relationship between gross operating income and the number of days in Accounts receivables, inventories and Accounts payables of Belgian firms. Deloof found out that for Belgian firms, there is a negative relationship between number of days accounts payable and gross profit income. He argues that this negative relationship is due to the fact that less profitable firms wait longer to pay their bills.

A similar research on the effect of working capital management on firms’ profitability was conducted by Dermigunes and Samiloglu (2008) on Turkish firms. They used a sample of manufacturing firms listed on the Instanbul Stock Exchange for the period of 1998-2007. Using multiple regression model, their empirical findings show that accounts receivables period, inventory period and leverage affect profitability negatively while growth in sales affects firm profitability positively.

In the Kenyan perspective, Kungu (2015) undertook a study of 81 manufacturing firms listed on the Nairobi stocks Exchange to determine the effects of working capital management on the profitability of manufacturing firms. Kungu (2015) used correlational research design and he justified its use by stating that it explores relationships to make predictions. The findings of his study showed that there was a positive linear relationship between credit policy and profitability. He concluded that profits can be enhanced if firms manage their accounts receivables in a more efficient way. His findings however contradict those of Deloof (2003) whose findings showed a negative relationship between gross operating income and the number of days in Accounts receivables. There is therefore need to research this topic further to solve the contradiction, and this is what this research study aims to do.

2.4.2 Cash ManagementAccording to Lantz (2008) managers have three motives for holding cash; transaction motive, speculative motive and precautionary motive. The transaction motive is whereby they keep cash to meet their own obligations such as payment to suppliers. Companies cannot depend on customers to pay on time because they can be late and pay after due date which will involve extra costs. The speculative motive is whereby due to the unpredictability of the market, opportunities could come up at any time and when they do, companies should have cash available to invest. Lastly is the precautionary motive where by just like the market is unpredictable so are the activities of the business. These activities include a sudden increase or decrease in demand, machine breakdowns which could occur and have a negative influence for the whole company if not taken care of (Lantz, 2008).

In their research paper, Lazaridis and Tryfonidis (2006) studied a sample of 131 companies listed in Greece on the Athens Stock Exchange for a period of four years between 2001 and 2004. The focus of the study was establishing whether there exists a relationship that is statistically significant between profitability and cash conversion cycle and its components (inventory, accounts payables and accounts receivables). They used the Pearson correlation and the pooled ordinary least squares (OLS) to analyze the relationship. They defined profitability as the gross operating profit. They found that lower gross operating profit is associated with an increase in the number of days of accounts payables. They also concluded that managers can create profits for their companies by handling correctly the cash conversion cycle and keeping each different component of cash conversion to an optimum level.

Biger et al (2010) conducted a research similar to Lazaridis and Tryfonidis’s (2006) though it differed in that it precisely looked at American Manufacturing firms listed on the New York Stock Exchange. They used a sample of 88 American firms for a period of three years from 2005 to 2007 to establish the relationship between working capital management and corporate profitability. They used regression analysis and like Lazaridis and Tryfonidis (2006) they too found a statistically significant relationship between cash conversion cycle and profitability measured as gross profit margin. Their conclusion is that profitability can be enhanced if firms manage their working capital efficiently.

Melita, Elfani and Petros (2010) empirically investigated the effect of working capital management on firm’s financial performance in emerging markets. Their sample consisted of firms listed in the Cyprus Stock Exchange between the periods 1998 to 2007. They used multivariate regression analysis, and the results reveal that working capital management leads to improved profitability. Specifically, the results indicate that the cash conversion cycle and all its major components (days in inventory, days sales outstanding and creditors payment period) are associated with the firm’s profitability. Their study covered all firms and not specifically on manufacturing firms. Different firms have their own unique characteristics and therefore, what favours one industry may not favour another industry. For example, manufacturing firms have to consider manufacturing plants that convert raw materials into finished goods while commercial industries don’t have plants since they only deal with finished goods. Therefore, assuming that the effect of working capital on profitability is similar for all industries is misleading.

Mwangi (2013) did a study on relationship between working capital management and financial performance of manufacturing firms quoted at the Nairobi Stock Exchange for the period of five years from 2007 to 2011. The study found out that Cash conversion cycle period and Net payment period has significant negative relation with return on Equities. It also found out that inventory turnover in days has negative relationship with ROE. However, this study did not evaluate whether independently, increase or decrease in current assets, current liabilities, current assets to total assets ratio has any effect on the profitability of the firm.

2.4.3 Accounts PayablesAccounts payables are incurred when a company purchases products for which payment will be made on a specified later date in the future. Accounts payables are one of the major sources of unsecured short term financing (Gitman, 2009). It is important for a firm to ensure that it has a good working relationship with its suppliers so that it can have a constant supply of inventories. One advantage of having trade credit from sellers is that a company can reduce some investment in working capital management and save some resource (Damodaran, 1997). A second advantage is that it’s available to all the companies regardless of the size of the company and early payment can bring cash discount with it. Maximizing account payable and stretching the payment term could form a competitive advantage for firms. However, the disadvantage of maximizing accounts payable by having a longer trade credit period from the suppliers is that firms may not get a discount from their vendors or they may end up getting lower quality products and services from suppliers. Companies should manage their accounts payables effectively as well as have the ability to generate enough cash to pay the mature account payables (Kungu, 2015).

Ponsian, Kiemi, Gwatako and Halim (2014) carried out a study to determine the effect of working capital management on company profitability. The study aimed at examining the statistical significance company’s working capital management and profitability. The study adopts quantitative approaches to test a series of research hypotheses. The researchers conducted the study on a sample of three manufacturing companies listed on the Dar es Salaam Stock Exchange (DSE) for a period of ten years between 2002 and 2012 with a total of 30 observations. Data was analyzed using Pearson’s correlation and Regression analysis. Among other finding the researchers found that there exists a highly significant positive relationship between average payment period and profitability. The gap in this study is that the key focus was on payment period, ignoring other aspects of accounts payables. The sample which comprised of only three manufacturing firms may be an inadequate representation of the entire industry in Tanzania, leave alone Kenya.
Ani et al. (2012) studied the effects of working capital management on profitability: evidence from the top five beer brewery firms in the world. They focused on working capital management as measured by the cash conversion cycle (CCC), and how the individual components of the CCC influence the profitability of world leading beer brewery firms. Multiple regression equations were applied to across sectional time series data. The study found that working capital management a represented by the cash conversion cycle, sales growth and lesser debtors’ collection period impacts on beer brewery firms’ profitability. His study however only focused on the inventory, payables and receivables turnover ratios and not their levels or their proportion to the total assets and liabilities. Further, the study only looked at only the top five beer companies in the world and hence this may not be a true representation of African Manufacturing firms.

From a local perspective, Mathuva (2010) used a sample of 30 firms listed on the Nairobi Stock Exchange in Kenya for a period of 16 years from 1993-2008 to examine the influence of working capital management components on corporate profitability. Using both the pooled OLS and the fixed effects regression models, Mathuva (2010) found that there exists a highly significant negative relationship between the time it takes for firms to collect cash from their customers and profitability. This finding however is contrary to the findings of other researchers such as Deloof (2003). Mathuva (2010) found a highly significant positive relationship between the time it takes to pay its creditors and profitability, implying that the longer a firm takes to pay its creditors, the more profitable it is. Deloof (2003) on the other hand found that there is a negative relationship between days accounts payable and profitability. The difference in the direction of impact in Kenyan firms and Belgian firms discussed above may be attributed to their different characteristics, there is however need for more research on the topic before a conclusion can be drawn on the cause of the difference.

2.4.4 Inventories conversion
Kulkanya (2012) study established effects of working capital management on the profitability of Thai listed firms. The regression analysis was based on a panel sample of 255 companies listed on the Stock Exchange of Thailand from 2007 to 2009. The results revealed a negative relationship between the gross operating profits and inventory conversion period and the receivables collection period. The study concluded that managers can increase the profitability of their firms by shortening the cash conversion cycle. This study was conducted in Thai in Asia and this may not be a true representation of African Manufacturing firms.

Alipour (2011) carried out a research on working capital management and corporate profitability. He took a sample of 1063 companies listed at the Tehran Stock Exchange. To test the hypothesis he used multiple regression and Pearson correlation. His analysis showed that sales and profits of a company can be greatly influenced by working capital management. His findings show a significant relationship between working capital management and profitability of a company. There is a negative relationship between inventory turnover in days, average collection period, cash conversion cycle and profitability. The findings of this study should however be used with caution when generalizing on private manufacturing companies because it only consists of data from huge companies that are listed at Tehran Stock Exchange.

Waithaka (2010) conducted a study on the relationship between working capital management practices and financial performance of Agricultural companies listed at the Nairobi Stock Exchange. The study adopted a Correlational Research Design which attempted to explore the relationship between working capital management and financial performance to make predictions. The study findings were that, financial performance is positively related to efficiency of inventory management, efficiency of cash management and efficiency of receivables management. The study’s gap however is that it focused on Agricultural firms. Further, the study focused on Average Collection period, Average payable period, and average collection period and debt ratio and not on the levels of current assets and liabilities.
Makori and Jagongo (2013) studied the relationship between working capital management and firms’ profitability. They used empirical evidence from manufacturing and construction firms listed on NSE, for the period 2003-2012. Pearson correlation and Ordinary Least Squares regression models were used to establish the relationship between working capital management and firm’s profitability. They found a positive relationship between profitability and number of day’s inventory as well as number of day’s payables. There was a negative relationship between profitability and number of day’s accounts receivable and the cash conversion cycle. The study however limited itself to current ratio, number of days accounts receivables, cash conversion cycle, number of days inventory and number of days payables. The researchers did not determine how levels of current assets and liabilities affect the financial performance of the firm.

2.5 Critique of the existing literatureA study carried out in Kenya by (Kungu, 2015) targeted manufacturing firms in Kenya. These results are however too general to be interpreted because manufacturing firms consists of both large enterprises and SME’s and different working capital components are bound to affect them differently. The results of his study are therefore too general to be interpreted in relation to both SME’s and large enterprises.
Makori and Jagongo (2013) carried out a study on the relationship between working capital management and profitability of manufacturing and construction listed on Nairobi Securities Exchange. The results were however based on secondary data where he used record survey sheet. Secondary data from financial statements give values at a specific date and hence require to be supplemented by primary data collected from opinions of finance managers. Mathuva (2010) conducted a similar research and also concentrated on firms listed in Nairobi Securities Exchange. Companies listed in NSE are large companies and this therefore excludes small companies. The results of both studies can therefore only be generalized on large and listed companies.
Ikram, Mohamad, Khalid and Zaheer (2011) researched on the effect of working capital management on the profitability of cement industries in Kenya. The results are based on a single sub sector of the manufacturing industry. The results should be used with caution and should only be generalized to the cement industry and not the entire manufacturing sector.
2.6 SummaryThe upshot of the literature review on working capital management is that while working capital components impact on financial performance of firms, there is ambiguity regarding the direction of the impact of different components on firms’ financial performance. The literature review considers four independent variables of working capital which are accounts receivables, cash management, accounts payables and inventories. There are conflicting conclusions on the relationship between working capital variables and their impact on financial performance of firms. For example Deloof (2003) study findings conclude a negative relationship between day sales inventory and profitability which is contrary to Mathuva (2010) whose study concludes a significant positive relationship on the same. This shows that there is no clear direction of the relationship between working capital variables and financial performance. This study focus is on private manufacturing firms which will help indicate whether the difference in the direction of the impact is affected by the nature of industries selected in different studies.

2.7 Research Gaps
Dellof and Jegers (1996) argued that large inventory and a generous trade credit policy may lead to high profitability. Zariyawati et al. (2009) on the other hand found that investment with a higher risk may create a higher return, therefore a firm with a high liquidity in working capital will have a low risk of failing to meet its obligations, will have low profitability at the same time. Sathamoorthi (2002) argues that increase in total assets to the total asset ratio has a negative effect on firm’s profitability while increase in current liabilities to total liabilities has a positive effect on profitability of firms. The conflicting arguments create a gap whereby some arguments favor high levels of current assets while others are of contrary suggestion. There is therefore need for a study to establish which argument is applicable in manufacturing firms in Kenya.

After carrying out an empirical review, the researcher realized that all the reviewed studies have focused on the effect of inventory collection period, debt collection period, payables period and debt ratio on performance of the firm. None of the reviewed literature has established whether or not and how the level of currents assets and current liabilities affect the financial performance of the firm. Further, some researchers have looked at the effect of working capital management of the performance of all forms combined, without studying each industry sector individually. Some findings are therefore misleading because industry sectors are different, and this may affect how their profitability is affected by working capital.

In relation to the above, while evaluating the effect of working capital management on the profitability of private manufacturing firms in Kenya, this study will mainly focus on the effect of the levels of current assets and current liabilities measured against total assets and total liabilities respectively. This will shed light as to which among Dellof and Jegers (1996) argument on inventory and credit policy, the risk and return trade off theory which argues that a firm can only chose liquidity or profitability at the expense of the other, Sathamoorthi Asset profitability theory which argues that increase in current assets to total assets ratio has a negative effect on a firm’s profitability while an increase in current liabilities to total liabilities has a positive effect on profitability of firms and the cash management theory that helps modern day firms to manage cash taking into consideration the fluctuations in daily cash flow best applies to manufacturing firms.

CHAPTER THREEMETHODOLOGY3.0 IntroductionThis chapter will describe the methodological design that will be used to achieve the objectives of the study. The chapter will discuss the research design and the justification of the chosen research design. The chapter will further describe the target population, sampling technique, sample size, research instruments, data collection procedures, data analysis and presentations.
3.1 Research DesignThis research adapted correlational research design because this design attempts to explore relationships and to make predictions. The design was used to identify, describe and show relationships and to analyze variables of working capital management that affect financial performance in private manufacturing firms in Kenya. This research design has successfully been used by Kaddumi and Ramadan (2012) in their research to investigate the effects of working capital management on profitability of Industrial firms in Jordan listed at Amman Stock Exchange.

3.2Population
This study’s target population was 311private manufacturing firms operating in Nairobi County. The study will focus exclusively on private manufacturing firms that deal with transformation of raw materials to finished products. The 311 firms operate in twelve major industry groups as shown in appendix II.

3.3 Sampling FrameSampling frame is a list of elements from which a sample is drawn (Cooper ; Schindler, 2011). For the purpose of this research, the sampling frame constitute the firms names contained in the KAM’S 2011 directory.

3.4 Sample Size and Sampling TechniqueKerlinger (1973) asserts that a sample size of 10% of a target population is large enough as long as it allows for reliable data analysis by cross tabulation, provides desired level of accuracy in estimates of the large population and allows for testing the significance of differences between the estimates. In studying the impact of working capital management on profitability of listed companies in Sri Lanka Jayarathne (2014) used the Naasiuma (2000) model to get the sample size from the total population of 39 listed companies.

Proportional allocation was used to determine the size of each sample for different strata. The sample was stratified into the twelve sub-sectors as per KAM 2011 directory classification. The sample was determined as shown in table 3.4 below:
Table 3.4: Determination of Sample Size
Category of Manufacturer Total No. %age Sample Size
Of Firms Ratio % age * 76
Building, Mining & Construction 12 3.86 3
Chemical & Allied Sector 36 11.58 9
Energy, Electrical & Electronics 23 7.40 7
Foods & Beverages Sector 58 18.64 14
Leather & Footwear Sector 1 0.32 0
Metal & Allied Sector 42 13.50 10
Motor Veh. Assembly & Accessories 23 7.40 6
Paper & Board Sector 36 11.58 9
Pharmaceutical & Med. Equip. 11 3.54 3
Sector Plastics & Rubber Sector 38 12.22 9
Textile & Apparels Sector 16 5.14 4
Timber, Wood & Furniture Sector 15 4.82 4
Total 311 100.00 78
The study used stratified random sampling technique in the selection of the sample. Bryman (2008), Cooper and Schindler (2011) and Saunders et al., (2007) assert that stratified random sampling technique is appropriate where most population can be segregated into several mutually exclusive sub-populations or strata.

3.5 Data collection InstrumentsThe researcher used secondary data in empirical analysis. (Kothari, 2004) explain that, secondary data means data that are already available. Secondary data may either be published or unpublished. Usually published data are available in public records and statistics, historical documents, and other sources of published information, technical trade journals among other sources. Unpublished data may be found in diaries, letters, unpublished biographies and autobiographies among other sources. A schedule was then be used to organize the data that will be collected.

3.6 Data Processing and AnalysisPanel data is the combinations of cross-sectional and times series data. Panel data is also called pooled data, micro panel data, longitudinal data, event history analysis and cohort analysis (Gujarati, 2003). The data will be collected, cleaned, sorted and coded using numerical numbers. It will then be entered in the Eviews statistical software for analysis.
3.6.1 Empirical ModelThe main types of data that are generally available for empirical analysis are cross section, time series and panel. In cross-section data, values of one or more variables are collected for several sample entities, or units, at the same point in time. In time series data observe the values of one or more variables over a period of time. In panel data the same cross-sectional units is surveyed over time (Marashdeh 2014). The panel data was considered as appropriate tool because panel data measure and detect appropriately the effects that cannot easily be detected by using pure cross-sectional data or pure time series data. Further, the panel data is flexible, it give more information on data analysis, it has more variability; it has less collinearity among variables and enhance efficiency (Gujarati, 2003).

The general multiple regression analysis was then used to estimate causal relationship between financial performance and the independent variables.

FPit=?0+?1ARit+?2APit+?3ICit+?4CMit+?it+?it ………………………. (3.2)
Where
FPitis financial performance i at time t
?0is the constant or intercept
?i;(i=1,2,3, 4) is coefficient of regression
ARitis independent variable, accounts receivables i at time t
APitis independent variable, accounts payables i at time t
ICitis independent variable, inventory control i at time t
CMitis independent variable, cash management i at time t
?itis the individual level effect
?itis the idiosyncratic error
3.7 Operationalization of VariableAccording to Ondigo (2016) Operationalization is the process of assigning numerals, numbers and other symbols to study variables. Operationalization involves the explicit specification of a variable in such a way that its measurement is possible.

Type Of Variable Variable Measure Notation
Dependent Financial Performance Net incomeTotal assetsX100% FP
Independent accounts receivables Account receivablesalesX 365 AR
Independent accounts payables Account payablecost of goods soldX 365 AP
Independent inventory control invetory cost of goods soldX 365 IC
Independent cash management (Average collection period+ inventory turnover)-average (payment period) CM
CHAPTER FOURRESULTS AND DISCUSSIONS4.1 Introduction
This chapter presents the descriptive statistics, unit root test, cointegration test, correlation results, Heteroskedasticy, autocorrelation and regression result.
4.2 Descriptive statistics
Table 4. SEQ Table * ARABIC 1 Descriptive statisticsReturn on assets Accounts receivables Cash management Account payable Inventory conversion
 Mean  0.174091  2.185227  0.602045  15.63591  0.053182
 Std. Dev.  0.222107  2.064588  0.437863  1.437272  0.241042
 Skewness  2.661172  2.698116  1.873311 -0.480821 -1.370673
 Kurtosis  12.10199  10.08522  6.113966  1.968062  9.974225
 Jarque-Bera  203.8182  145.4194  43.51224  3.647694  102.9504
 Probability  0.000000  0.000000  0.000000  0.161404  0.000000
4.2.1 Accounts receivables
The measures used were mean, median, maximum and minimum value, standard deviation, skewness, kurtosis and Jarque-Bera (JB). Positive and low performance mean of 2.185227% is associates with less volatility of the series. The standard deviation of; 2.064588% is high. Stock return portrays a positive Skewness 2.698116 indicating a right tail of distribution which indicate that the variable is asymmetry. Kurtosis value was 10.08522 which is >3, which shows that the variable is normally distributed. Furthermore, significant JB value (145.4194) shows that the variable is normally distributed.
4.2.2 Cash management
The measures used were mean, median, maximum and minimum value, standard deviation, skewness, kurtosis and Jarque-Bera (JB). Positive and low performance mean of 0.602045 % is associates with less volatility of the series. The standard deviation of; 0.437863 % is high. Stock return portrays a positive Skewness 1.873311 indicating a right tail of distribution which indicate that the variable is asymmetry. Kurtosis value was  6.113966 which is >3, which shows that the variable is normally distributed. Furthermore, significant JB value ( 43.51224) shows that the variable is normally distributed.
4.2.3 Account payable
The measures used were mean, median, maximum and minimum value, standard deviation, skewness, kurtosis and Jarque-Bera (JB). Positive and low performance mean of 15.63591 % is associates with less volatility of the series. The standard deviation of; 1.437272 % is high. Stock return portrays a negative Skewness -0.480821 indicating a right tail of distribution which indicate that the variable is asymmetry. Kurtosis value was 10.08522 which is >3, which shows that the variable is normally distributed. Furthermore, significant JB value (3.647694) shows that the variable is normally distributed.
4.2.4 Inventory conversionThe measures used were mean, median, maximum and minimum value, standard deviation, skewness, kurtosis and Jarque-Bera (JB). Positive and low performance mean of 0.053182 % is associates with less volatility of the series. The standard deviation of; 0.241042% is high. Stock return portrays a negative Skewness -1.370673 indicating a right tail of distribution which indicate that the variable is asymmetry. Kurtosis value was 9.974225which is >3, which shows that the variable is normally distributed. Furthermore, significant JB value (102.9504) shows that the variable is normally distributed.
4.2.5 Financial performance
The measures used were mean, median, maximum and minimum value, standard deviation, skewness, kurtosis and Jarque-Bera (JB). Positive and low performance mean of 0.174091% is associates with less volatility of the series. The standard deviation of; 0.222107 % is high. Stock return portrays a positive Skewness 2.661172 indicating a right tail of distribution which indicate that the variable is asymmetry. Kurtosis value was 12.10199 which is >3, which shows that the variable is normally distributed. Furthermore, significant JB value (203.8182) shows that the variable is normally distributed.
4.3 Unit root test4.3.1 Financial PerformanceTable 4. SEQ Table * ARABIC 2 Financial PerformanceMethod Statistic P-value
Levin, Lin & Chu t -20.7962  0.0000
Im, Pesaran and Shin W-stat  -6.86305  0.0000
ADF – Fisher Chi-square  48.0058  0.0002
PP – Fisher Chi-square  56.6539  0.0000
Table 4.2 resents the unit roots tests of the dependent variable Financial Performance. Particularly the table presents the results of one test of unit roots in a panels setting. From the test results the Levin, Lin & Chu t -20.7962 and p-value 0.0000 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. Im, Pesaran and Shin W-stat  -6.86305 and p-value 0.0000 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. ADF – Fisher Chi-square 48.0058 and p-value 0.0002 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. PP – Fisher Chi-square 56.6539 and p-value 0.0000 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. The probabilities are very significant implying that we do reject the null hypothesis of a unit root problem. All the above unit roots methods show that the variables were stationary at level.

4.3.2 Account ReceivableTable 4. SEQ Table * ARABIC 3 Account ReceivableMethod Statistic P-value
Levin, Lin & Chu t -81.2622  0.0000
Im, Pesaran and Shin W-stat  -26.0607  0.0000
ADF – Fisher Chi-square  76.3473  0.0000
PP – Fisher Chi-square  76.9487  0.0000

Table 4.3 resents the unit roots tests of the independent variable Account Receivable. Particularly the table presents the results of one test of unit roots in a panels setting. From the test results the Levin, Lin & Chu t -81.2622 and p-value 0.0000 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. Im, Pesaran and Shin W-stat  -26.0607 and p-value 0.0000 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. ADF – Fisher Chi-square 76.3473 and p-value 0.0002 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. PP – Fisher Chi-square 76.9487 and p-value 0.0000 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. The probabilities are very significant implying that we do reject the null hypothesis of a unit root problem. All the above unit roots methods show that the variables were stationary at level.

4.3.3 Cash ManagementTable 4. SEQ Table * ARABIC 4 Cash Management
Method Statistic P-value
Levin, Lin & Chu t -77.2886  0.0000
Im, Pesaran and Shin W-stat  -25.3086  0.0000
ADF – Fisher Chi-square  79.3229  0.0000
PP – Fisher Chi-square  88.5918  0.0000
Table 4.4 resents the unit roots tests of the independent variable Cash Management. Particularly the table presents the results of one test of unit roots in a panels setting. From the test results the Levin, Lin & Chu t -77.2886 and p-value 0.0000 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. Im, Pesaran and Shin W-stat  -25.3086 and p-value 0.0000 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. ADF – Fisher Chi-square 79.3229 and p-value 0.0000 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. PP – Fisher Chi-square 88.5918 and p-value 0.0000 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. The probabilities are very significant implying that we do reject the null hypothesis of a unit root problem. All the above unit roots methods show that the variables were stationary at level.

4.3.4 Account PayableTable 4. SEQ Table * ARABIC 5 Account Payable
Method Statistic P-value
Levin, Lin & Chu t -36.8215  0.0000
Im, Pesaran and Shin W-stat  -15.3358  0.0000
ADF – Fisher Chi-square  66.6999  0.0000
PP – Fisher Chi-square  66.8901  0.0000
Table 4.5 resents the unit roots tests of the independent variable Account Payable. Particularly the table presents the results of one test of unit roots in a panels setting. From the test results the Levin, Lin & Chu t -36.8215 and p-value 0.0000 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. Im, Pesaran and Shin W-stat  -15.3358 and p-value 0.0000 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. ADF – Fisher Chi-square 66.6999 and p-value 0.0000 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. PP – Fisher Chi 66.8901 and p-value 0.0000 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. The probabilities are very significant implying that we do reject the null hypothesis of a unit root problem. All the above unit roots methods show that the variables were stationary at level.

4.3.5 Inventory ManagementTable 4. SEQ Table * ARABIC 6 Inventory Management
Method Statistic P-value
Levin, Lin & Chu t* -3.38463  0.0004
Im, Pesaran and Shin W-stat  -1.63962  0.0505
ADF – Fisher Chi-square  26.3447  0.0921
PP – Fisher Chi-square  31.6118  0.0244
Table 4.6 resents the unit roots tests of the independent variable Account Payable. Particularly the table presents the results of one test of unit roots in a panels setting. From the test results the Levin, Lin & Chu t -3.38463 and p-value 0.0004 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. Im, Pesaran and Shin W-stat  -1.63962 and p-value 0.0505 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. ADF – Fisher Chi-square 26.3447 and p-value 0.0921 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. PP – Fisher Chi 31.6118and p-value 0.0244 test statistics reveals that the null hypothesis that the variable is stationary at level was not rejected. The probabilities are very significant implying that we do reject the null hypothesis of a unit root problem. All the above unit roots methods show that the variables were stationary at level.

4.4 Correlation AnalysisTable 4. SEQ Table * ARABIC 7: Correlation AnalysisReturn on assets Accounts receivables Cash management Account payable Inventory conversion
Return on assets  1.000000 Accounts receivables  0.601664  1.000000 Cash management -0.152915 -0.285414  1.000000 Account payable -0.290093 -0.404640 -0.455171  1.000000  0.105160
Inventory conversion  0.103396  0.069539 -0.074055  0.105160  1.000000
Table 4.7 presents the results on correlation analysis of the five variables. From the results the correlation analysis shows that there was moderate correlation between account payable and account receivables of -0.404640. The correlation between cash management and account payable value of -0.455171 is moderate. The correlation between cash management and account receivable of -0.285414 was found to be low. The correlation between account payable and inventory conversion of 0.105160 was found to be low. The correlation between cash management and inventory conversion of -0.074055 was found to be low. The correlation between cash management and inventory conversion of -0.074055 was found to be low. These results shows that the level of muticollineariry is very low. This would allow for regression analysis to be conducted efficiently.

4.5 Cointegration test
Engle and Granger (1987) note that, cointegration test is based on an examination of the residuals of a spurious regression performed using I(1) variables. If the variables are cointegrated then the residuals should be integrated of order zero I(0). On the other hand if the variables are not cointegrated then the residuals will be integrated of order one I(1).

Table 4. SEQ Table * ARABIC 8: Kao Residual Cointegration TestKao Residual Cointegration Test t-Statistic Prob.

ADF -2.940228  0.0016
Residual variance  0.037799 HAC variance  0.017433 From table 4.8 the Kao Residual Cointegration Test t-statistic value of -2.940228 and the p-value of 0.0016 implies that is cointegration among the various measures of working capital and financial performance.
4.6 HeteroskedasticityTable 4. SEQ Table * ARABIC 9: HeteroskedasticityHeteroskedasticity Test: Breusch-Pagan-Godfrey
F-statistic 4.815348     Prob. F(4,39) 0.3730
Obs*R-squared 14.54653     Prob. Chi-Square(4) 0.2957
Scaled explained SS 40.51622     Prob. Chi-Square(4) 0.3600
Table 4.9 presents the result after the testing of heteroskedasticity using Breusch-Pagan-Godfrey test. The test statistics value of F-statistic4.815348, Obs*R-squared14.54653 and Scaled explained SS40.51622 were statistically insignificant and the presence of heteroskedasticity was rejected. The interpretation was that the estimation of the regression model needed special methods that could eliminate the tendency of the variance and mean to change over time.
4.7 AutocorrelationTable 4. SEQ Table * ARABIC 10: AutocorrelationBreusch-Godfrey Serial Correlation LM Test: F-statistic 0.935780     Prob. F(2,37) 0.4014
Obs*R-squared 2.118481     Prob. Chi-Square(2) 0.3467
Table 4.10 Presents the results for the test of serial correlation. The test results does not reject the null of no autocorrelation up to order. The null hypothesis was not rejected on the bases that the p-value of the two test statistics was statistically significant. The F-statistic value of 0.935780 and the p-value of 0.4014 indicates that the residuals were serially uncorrelated. The Obs*R-squared value of 2.118481 and the p-value of 0.3467 shows that there was no autocorrelation.

4.8 Variance inflated factorTable 4. SEQ Table * ARABIC 11: Variance inflated factorVariance Inflation Factors
Coefficient Uncentered
Variable Variance VIF
Accounts receivables  0.000148  2.023455
Cash management  0.007509  2.218874
Account payable  0.000579  1.894251
Inventory conversion  0.002020  1.173148
Table 4.11, presents the variance inflated factor results. From the results the VIF are bellow the value of 5. The interpretation of the findings was that there was no multicollineality problem.

4.9 Regression resultsGood-of- fit Statistics
The results in Table 4.12, indicates that the overall model was a good fit under the Panel Dynamic Least Squares (DOLS). From table 4.12 the value of the R-squared 0.584223 and Adjusted R-squared 0.423277. This value clearly suggests that after adjusting for the degrees of freedom there is a relationship between Accounts receivables, Cash management, Account payable and Inventory conversion. This indicates that Accounts receivables, Cash management, Account payable and Inventory conversion causes a variation of 0.423277 % on financial performance of manufacturing companies.
Table 4. SEQ Table * ARABIC 12: Regression Coefficients
Dependent Variable: Financial performance Method: Panel Dynamic Least Squares (DOLS) Variable Coefficient Std. Error t-Statistic Prob.  
Accounts receivables 0.050838 0.012180 4.173923 0.0002
Cash management 0.234850 0.086655 2.710162 0.0109
Account payable 0.039632 0.024069 1.646559 0.1098
Inventory conversion -0.102616 0.044943 -2.283265 0.0294
R-squared 0.584223     Mean dependent var 0.174091
Adjusted R-squared 0.423277     S.D. dependent var 0.222107
S.E. of regression 0.168673     Sum squared resid 0.881972
Long-run variance 0.012397 4.9.1 Accounts receivablesFrom table 4.12 the regression coefficient of Accounts receivables was found to be 0.050838. This value shows that holding other variables in the model constant, an increase in Accounts receivables by one unit causes the financial performance to increase by 0.050838 units. The value of the coefficient is also positive. The coefficient was positive and statistically significant with a t-statistic value of 4.173923 and the standard error was found to be 0.012180 and the p-value was found to be 0.0002. Kungu (2015) used correlational research design and he justified its use by stating that it explores relationships to make predictions. The findings of his study showed that there was a positive linear relationship between credit policy and profitability. He concluded that profits can be enhanced if firms manage their accounts receivables in a more efficient way. His findings however contradict those of Deloof (2003) whose findings showed a negative relationship between gross operating income and the number of days in Accounts receivables. There is therefore need to research this topic further to solve the contradiction, and this is what this research study aims to do.
4.9.2 Cash managementFrom table 4.12 the regression coefficient of Cash management was found to be 0.234850. This value shows that holding other variables in the model constant, an increase in Cash management by one unit causes the financial performance to increase by 0.234850 units. The value of the coefficient is also positive. The coefficient was positive and statistically significant with a t-statistic value of 2.710162and the standard error was found to be 0.086655 and the p-value was found to be 0.0109. Mwangi (2013) did a study on relationship between working capital management and financial performance of manufacturing firms quoted at the Nairobi Stock Exchange for the period of five years from 2007 to 2011. The study found out that Cash conversion cycle period and Net payment period has significant negative relation with return on Equities. It also found out that inventory turnover in days has negative relationship with ROE. However, this study did not evaluate whether independently, increase or decrease in current assets, current liabilities, current assets to total assets ratio has any effect on the profitability of the firm.
4.9.3 Account payableFrom table 4.12 the regression coefficient of Account payable was found to be 0.039632. This value shows that holding other variables in the model constant, an increase in Account payable by one unit causes the financial performance to increase by 0.039632units. The value of the coefficient is also positive. The coefficient was positive and statistically significant with a t-statistic value of 1.646559 and the standard error was found to be 0.024069 and the p-value was found to be 0.1098. Mathuva (2010) found a highly significant positive relationship between the time it takes to pay its creditors and profitability, implying that the longer a firm takes to pay its creditors, the more profitable it is. Deloof (2003) on the other hand found that there is a negative relationship between days accounts payable and profitability. The difference in the direction of impact in Kenyan firms and Belgian firms discussed above may be attributed to their different characteristics, there is however need for more research on the topic before a conclusion can be drawn on the cause of the difference.
4.9.4 Inventory conversion
From table 4.12 the regression coefficient of Account payable was found to be -0.102616. This value shows that holding other variables in the model constant, an increase in Account payable by one unit causes the financial performance to decrease by 0.102616 units. The value of the coefficient is also negative. The coefficient was negative and statistically significant with a t-statistic value of -2.283265 and the standard error was found to be 0.044943 and the p-value was found to be 0.0294. Waithaka (2010), conducted a study on the relationship between working capital management practices and financial performance of Agricultural companies listed at the Nairobi Stock Exchange. The study adopted a Correlational Research Design which attempted to explore the relationship between working capital management and financial performance to make predictions. The study findings were that, financial performance is positively related to efficiency of inventory management, efficiency of cash management and efficiency of receivables management. Makori and Jagongo (2013), studied the relationship between working capital management and firms’ profitability. They used empirical evidence from manufacturing and construction firms listed on NSE, for the period 2003-2012. Pearson correlation and Ordinary Least Squares regression models were used to establish the relationship between working capital management and firm’s profitability. They found a positive relationship between profitability and number of day’s inventory as well as number of day’s payables.

CHAPTER FIVESUMMARY, CONCLUSIONS AND RECOMMENDATIONS5.1 IntroductionThis chapter presents summary of the major findings, conclusion, and recommendations and gives directions for further research. In particular the chapter discusses the findings on normality test, stationarity test, correlation test, cointegration test and regression results. The main objective was to reveal the effect of working capital management on financial performance in Kenya.
5.2 Summary of findings5.2.1 Effect of Accounts receivables on financial performanceThe study utilized various techniques in an attempt to diagnose the properties of Accounts receivables as a variable. The findings descriptive statistic results showed that the Accounts receivables was not normally distributed. The four unit root statistics showed that Accounts receivables variable had no unit root thus it was stationary. The unit root test was important in order to establish the appropriate regression technique method for regression analysis. The study revealed that the variable was stationary. The study also conducted correlation analysis between Accounts receivables and each of the other independent variables.
5.2.2 Effect of Cash management on financial performanceThe study utilized various techniques in an attempt to diagnose the properties of Cash management as a variable. The findings descriptive statistic results showed that the Cash management was not normally distributed. The four unit root statistics showed that Cash management variable had no unit root thus it was stationary. The unit root test was important in order to establish the appropriate regression technique method for regression analysis. The study revealed that the variable was stationary. The study also conducted correlation analysis between Cash management and each of the other independent variables.
5.2.3 Effect of Account payable on financial performance
The study utilized various techniques in an attempt to diagnose the properties of Account payable as a variable. The findings descriptive statistic results showed that the Account payable was not normally distributed. The four unit root statistics showed that Account payable variable had no unit root thus it was stationary. The unit root test was important in order to establish the appropriate regression technique method for regression analysis. The study revealed that the variable was stationary. The study also conducted correlation analysis between Account payable and each of the other independent variables.
5.2.4 Effect of Inventory conversion on financial performance
The study utilized various techniques in an attempt to diagnose the properties of Account payable as a variable. The findings descriptive statistic results showed that the Account payable was not normally distributed. The four unit root statistics showed that Account payable variable had no unit root thus it was stationary. The unit root test was important in order to establish the appropriate regression technique method for regression analysis. The study revealed that the variable was stationary. The study also conducted correlation analysis between Account payable and each of the other independent variables.
5.3 ConclusionAccounts receivables variable was found to have a positive and statistically significant effect on financial performance in Kenya. The study, thus concluded that Accounts receivables is an important variable in the determination of financial performance manufacturing firms in Kenya. The implication was that when the Accounts receivables, the firm amassed more resources to continue with its operations and consequently realized increases in financial performance. Cash management variable was found to have a positive and statistically significant effect on financial performance in Kenya. The study, thus concluded that Cash management is an important variable in the determination of financial performance manufacturing firms in Kenya. The implication was that for tCash management, the firm amassed more resources to continue with its operations and consequently realized increases in financial performance.
Account payable variable was found to have a positive and statistically significant effect on financial performance of private manufacturing firms in Kenya. The study, thus concluded that Account payable is an important variable in the determination of financial performance manufacturing firms in Kenya. The implication was that when the Account payable, the firm amassed more resources to continue with it operations and consequently realized increases in financial performance. Inventory conversion variable was found to have a negative and statistically significant effect on financial performance in Kenya. The study, thus concluded that Inventory conversion is an important variable in the determination of financial performance of manufacturing firms in Kenya. The implication was that with the Inventory conversion, the firm amassed more resources to continue with its operations and consequently realized increases in financial performance.
5.4 RecommendationThis research brings to light the effects of working capital management on financial performance of private manufacturing firms in Kenya. From the results of the study, I recommend the following:-
I recommend Inventory and Financial managers of manufacturing firms utilize the findings of this research in designing working capital management reform models. The Account payable variable was found to have a positive and statistically significant effect on financial performance of manufacturing firms in Kenya. The study therefore recommends Financial and Inventory managers to ensure they maintain good professional relationship with their suppliers by paying their credit purchases as agreed. Early payment is recommended because besides ensuring a good relationship with the suppliers, firms also get to enjoy discounts hence cutting down costs and consequently increasing profits.

The study recommends that inventory managers keeps the inventory conversion period short. Inventory conversion was found to have a negative and statistically significant effect on the financial performance of manufacturing firms in Kenya. Firms should put in place good economic inventory control systems such as just in time and Economic Order Quantity that minimizes the cost incurred. The Inventory managers should come up with measures that ensure inventory levels are maintained at an optimum level and that accurate inventory records are maintained.
The study recommends that the Board of Directors and Financial managers come up with ideal credit policies so as to minimize the risk of bad debts. Ideal credit policies should enhance that the firms are able to collect debts from their debtors and still maintain a good working relationship with them. Taking into account that this study found that there is a positive and statistically significant effect on financial performance of manufacturing firms in Kenya, firms should come up with policies that encourages debtors to pay their debts in the shortest time so that the firms can quickly reinvest the cash.

5.5 Area for further researchThis research was not able to identify all the possible variables with explanation power on financial performance of manufacturing firms in Kenya. The study was only able to explain about R-squared 0.584223 or 58.4223% and Adjusted R-squared 0.423277 or 42.3277% of the variations on financial performance of manufacturing firms in Kenya. The study thus recommends that future research should consider other factors that may affect the financial performance of manufacturing firms in Kenya.

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DATA: COLLECTION SCHEDULE
ROA Accounts receivables Account payable
Cash management Inventory conversion
0.19 101 108.67 26.81 34.48
0.12 77.76 87.72 22.67 32.63
0.11 89.28 71.45 59.87 42.04
0.06 107.65 96.62 66.34 55.31
-0.08 114.95 170.1 31.33 86.48
0.07 51.72 37.36 129.45 115.08
0.02 54.4 53.5 161.07 160.18
-0.24 50 71.43 146.53 167.96
0.13 46.92 80.81 170.12 204.01
0.11 53.05 69.48 126.67 143.1
0.08 97.81 100.6 117.5 120.3
0.07 109.36 89.53 126.51 106.68
0.07 82.71 125.28 50.83 93.39
0.07 61.77 114.19 95.68 148.1
0.07 71.67 81.61 76.1 86.04
0.3 24.33 75.86 -43.2 8.34
0.23 21.92 103.13 -11.54 69.67
0.25 14.9 57.45 18.07 60.62
0.17 16.67 81.82 8.89 74.04
0.13 16 75.21 17.74 76.95
0.23 61.57 123.69 34.75 96.88
0.38 50.22 108.38 37.44 95.61
0.24 43.16 83.07 62.68 102.59
0.33 29.88 123.7 40.65 134.47
0.31 38.12 156.71 19.86 138.45
0.12 102.96 204.86 22.75 124.65
0.06 84.99 251.25 -20.93 145.34
0.12 106.11 256.52 -41.25 109.17
0.14 69.74 325.97 -128.97 127.26
0.12 94.98 332.26 -112.52 124.76
0.27 90.21 71.34 87 68.13
1.14 63.07 70.77 92.23 99.94
0.93 93.51 45.23 98.66 50.37
0.27 73.01 44.12 56.03 27.14
0.29 57.29 59.04 31.91 33.66
0.15 126.82 79.99 204.05 157.23
0.06 110.24 75.29 128.64 93.7
0.09 121.02 85.94 106.96 71.88
0.12 174.39 167.15 120.19 112.96
0.09 0 0 0 0
0.16 30.76 78.61 4.15 52
-0.02 26.07 72.34 12.59 58.86
0.04 34.61 76.22 30.95 72.56
0.09 39.95 158.91 -2.68 116.28
0.17 16.67 81.82 8.89 74.04
0.23 21.92 103.13 -11.54 69.67
0.07 51.72 37.36 129.45 115.08
0.15 126.82 79.99 204.05 157.23
0.33 29.88 123.7 40.65 134.47
0.07 109.36 89.53 126.51 106.68
0.06 107.65 96.62 66.34 55.31
0.07 82.71 125.28 50.83 93.39
0.23 61.57 123.69 34.75 96.88
0.12 77.76 87.72 22.67 32.63
0.13 46.92 80.81 170.12 204.01
0.02 54.4 53.5 161.07 160.18
0.08 97.81 100.6 117.5 120.3
0.09 39.95 158.91 -2.68 116.28
0.19 101 108.67 26.81 34.48
0.17 16.67 81.82 8.89 74.04
0.23 21.92 103.13 -11.54 69.67
0.25 14.9 57.45 18.07 60.62
0.3 24.33 75.86 -43.2 8.34
0.24 43.16 83.07 62.68 102.59
0.38 50.22 108.38 37.44 95.61
0.33 29.88 123.7 40.65 134.47
0.13 16 75.21 17.74 76.95
0.23 61.57 123.69 34.75 96.88
0.12 102.96 204.86 22.75 124.65
0.14 69.74 325.97 -128.97 127.26
0.06 84.99 251.25 -20.93 145.34
0.31 38.12 156.71 19.86 138.45
0.12 106.11 256.52 -41.25 109.17
0.27 73.01 44.12 56.03 27.14
0.12 94.98 332.26 -112.52 124.76
0.27 90.21 71.34 87 68.13
1.14 63.07 70.77 92.23 99.94
0.93 93.51 45.23 98.66 50.37
0.06 110.24 75.29 128.64 93.7
0.12 174.39 167.15 120.19 112.96
0.09 121.02 85.94 106.96 71.88
0.15 126.82 79.99 204.05 157.23
0.29 57.29 59.04 31.91 33.66
0.09 39.95 158.91 -2.68 116.28
-0.02 26.07 72.34 12.59 58.86
0.09 0 0 0 0
0.16 30.76 78.61 4.15 52
0.04 34.61 76.22 30.95 72.56
0.11 89.28 71.45 59.87 42.04
0.06 107.65 96.62 66.34 55.31
0.19 101 108.67 26.81 34.48
0.12 77.76 87.72 22.67 32.63
0.07 51.72 37.36 129.45 115.08
-0.08 114.95 170.1 31.33 86.48
0.02 54.4 53.5 161.07 160.18
-0.24 50 71.43 146.53 167.96
0.13 46.92 80.81 170.12 204.01
0.07 82.71 125.28 50.83 93.39
0.07 109.36 89.53 126.51 106.68
0.07 61.77 114.19 95.68 148.1
0.11 53.05 69.48 126.67 143.1
0.08 97.81 100.6 117.5 120.3
0.17 16.67 81.82 8.89 74.04
0.23 21.92 103.13 -11.54 69.67
0.25 14.9 57.45 18.07 60.62
0.07 71.67 81.61 76.1 86.04
0.3 24.33 75.86 -43.2 8.34
0.38 50.22 108.38 37.44 95.61
0.33 29.88 123.7 40.65 134.47
0.23 61.57 123.69 34.75 96.88
0.24 43.16 83.07 62.68 102.59
0.13 16 75.21 17.74 76.95
0.31 38.12 156.71 19.86 138.45
0.14 69.74 325.97 -128.97 127.26
0.12 102.96 204.86 22.75 124.65
0.12 106.11 256.52 -41.25 109.17
0.06 84.99 251.25 -20.93 145.34
1.14 63.07 70.77 92.23 99.94
0.27 73.01 44.12 56.03 27.14
0.93 93.51 45.23 98.66 50.37
0.12 94.98 332.26 -112.52 124.76
0.27 90.21 71.34 87 68.13
0.06 110.24 75.29 128.64 93.7
0.15 126.82 79.99 204.05 157.23
0.12 174.39 167.15 120.19 112.96
0.29 57.29 59.04 31.91 33.66
0.09 121.02 85.94 106.96 71.88
0.16 30.76 78.61 4.15 52
0.09 0 0 0 0
0.09 39.95 158.91 -2.68 116.28
0.04 34.61 76.22 30.95 72.56
-0.02 26.07 72.34 12.59 58.86
-0.24 50 71.43 146.53 167.96
0.23 21.92 103.13 -11.54 69.67
0.31 38.12 156.71 19.86 138.45
0.11 53.05 69.48 126.67 143.1
0.17 16.67 81.82 8.89 74.04
0.14 69.74 325.97 -128.97 127.26
0.3 24.33 75.86 -43.2 8.34
0.19 101 108.67 26.81 34.48
0.25 14.9 57.45 18.07 60.62
0.11 89.28 71.45 59.87 42.04
0.07 51.72 37.36 129.45 115.08
0.13 46.92 80.81 170.12 204.01
0.07 82.71 125.28 50.83 93.39
0.12 94.98 332.26 -112.52 124.76
0.93 93.51 45.23 98.66 50.37
0.06 107.65 96.62 66.34 55.31
0.23 61.57 123.69 34.75 96.88
0.04 34.61 76.22 30.95 72.56
0.33 29.88 123.7 40.65 134.47
0.27 73.01 44.12 56.03 27.14
0.12 102.96 204.86 22.75 124.65
0.12 174.39 167.15 120.19 112.96
0.06 84.99 251.25 -20.93 145.34
0.07 109.36 89.53 126.51 106.68
-0.02 26.07 72.34 12.59 58.86
0.09 0 0 0 0
0.29 57.29 59.04 31.91 33.66
0.12 77.76 87.72 22.67 32.63
0.09 39.95 158.91 -2.68 116.28
0.24 43.16 83.07 62.68 102.59
0.07 71.67 81.61 76.1 86.04
1.14 63.07 70.77 92.23 99.94
0.16 30.76 78.61 4.15 52
0.27 90.21 71.34 87 68.13
0.02 54.4 53.5 161.07 160.18
0.07 61.77 114.19 95.68 148.1
0.13 16 75.21 17.74 76.95
0.09 121.02 85.94 106.96 71.88
0.12 106.11 256.52 -41.25 109.17
-0.08 114.95 170.1 31.33 86.48
0.38 50.22 108.38 37.44 95.61
0.08 97.81 100.6 117.5 120.3
0.15 126.82 79.99 204.05 157.23
0.06 110.24 75.29 128.64 93.7
0.07 109.36 89.53 126.51 106.68
-0.02 26.07 72.34 12.59 58.86
0.27 90.21 71.34 87 68.13
0.38 50.22 108.38 37.44 95.61
0.12 77.76 87.72 22.67 32.63
0.12 106.11 256.52 -41.25 109.17
0.09 39.95 158.91 -2.68 116.28
0.25 14.9 57.45 18.07 60.62
0.93 93.51 45.23 98.66 50.37
0.09 0 0 0 0
0.31 38.12 156.71 19.86 138.45
0.07 71.67 81.61 76.1 86.04
0.23 61.57 123.69 34.75 96.88
0.13 16 75.21 17.74 76.95
0.29 57.29 59.04 31.91 33.66
0.3 24.33 75.86 -43.2 8.34
0.27 73.01 44.12 56.03 27.14
-0.24 50 71.43 146.53 167.96
0.33 29.88 123.7 40.65 134.47
0.07 82.71 125.28 50.83 93.39
0.06 107.65 96.62 66.34 55.31
0.12 102.96 204.86 22.75 124.65
0.11 53.05 69.48 126.67 143.1
0.04 34.61 76.22 30.95 72.56
0.06 84.99 251.25 -20.93 145.34
0.17 16.67 81.82 8.89 74.04
0.12 174.39 167.15 120.19 112.96
0.24 43.16 83.07 62.68 102.59
0.07 61.77 114.19 95.68 148.1
0.06 110.24 75.29 128.64 93.7
1.14 63.07 70.77 92.23 99.94
0.23 21.92 103.13 -11.54 69.67
0.14 69.74 325.97 -128.97 127.26
0.08 97.81 100.6 117.5 120.3
0.19 101 108.67 26.81 34.48
-0.08 114.95 170.1 31.33 86.48
0.16 30.76 78.61 4.15 52
0.12 94.98 332.26 -112.52 124.76
0.13 46.92 80.81 170.12 204.01
0.02 54.4 53.5 161.07 160.18
0.15 126.82 79.99 204.05 157.23
0.11 89.28 71.45 59.87 42.04
0.09 121.02 85.94 106.96 71.88
0.07 51.72 37.36 129.45 115.08
0.11 53.05 69.48 126.67 143.1
1.14 63.07 70.77 92.23 99.94
0.14 69.74 325.97 -128.97 127.26
0.93 93.51 45.23 98.66 50.37
0.07 109.36 89.53 126.51 106.68
0.3 24.33 75.86 -43.2 8.34
0.15 126.82 79.99 204.05 157.23
0.02 54.4 53.5 161.07 160.18
0.06 110.24 75.29 128.64 93.7
0.12 77.76 87.72 22.67 32.63
0.33 29.88 123.7 40.65 134.47
-0.02 26.07 72.34 12.59 58.86
0.31 38.12 156.71 19.86 138.45
0.04 34.61 76.22 30.95 72.56
0.12 106.11 256.52 -41.25 109.17
0.24 43.16 83.07 62.68 102.59
0.12 94.98 332.26 -112.52 124.76
0.19 101 108.67 26.81 34.48
0.13 16 75.21 17.74 76.95
0.07 61.77 114.19 95.68 148.1
0.38 50.22 108.38 37.44 95.61
0.09 39.95 158.91 -2.68 116.28
0.07 71.67 81.61 76.1 86.04
-0.08 114.95 170.1 31.33 86.48
0.17 16.67 81.82 8.89 74.04
0.06 107.65 96.62 66.34 55.31
0.27 90.21 71.34 87 68.13
-0.24 50 71.43 146.53 167.96
0.07 82.71 125.28 50.83 93.39
0.06 84.99 251.25 -20.93 145.34
0.23 61.57 123.69 34.75 96.88
0.07 51.72 37.36 129.45 115.08
0.13 46.92 80.81 170.12 204.01
0.16 30.76 78.61 4.15 52
0.23 21.92 103.13 -11.54 69.67
0.09 0 0 0 0
0.29 57.29 59.04 31.91 33.66
0.08 97.81 100.6 117.5 120.3
0.09 121.02 85.94 106.96 71.88
0.12 174.39 167.15 120.19 112.96
0.11 89.28 71.45 59.87 42.04
0.12 102.96 204.86 22.75 124.65
0.27 73.01 44.12 56.03 27.14
0.25 14.9 57.45 18.07 60.62
0.04 34.61 76.22 30.95 72.56
0.12 94.98 332.26 -112.52 124.76
0.11 53.05 69.48 126.67 143.1
0.07 61.77 114.19 95.68 148.1
0.15 126.82 79.99 204.05 157.23
0.3 24.33 75.86 -43.2 8.34
0.27 90.21 71.34 87 68.13
0.09 39.95 158.91 -2.68 116.28
0.38 50.22 108.38 37.44 95.61
0.07 51.72 37.36 129.45 115.08
1.14 63.07 70.77 92.23 99.94
0.17 16.67 81.82 8.89 74.04
0.09 0 0 0 0
0.12 77.76 87.72 22.67 32.63
0.11 89.28 71.45 59.87 42.04
0.12 102.96 204.86 22.75 124.65
0.16 30.76 78.61 4.15 52
0.19 101 108.67 26.81 34.48
0.29 57.29 59.04 31.91 33.66
0.06 107.65 96.62 66.34 55.31
0.06 110.24 75.29 128.64 93.7
0.12 174.39 167.15 120.19 112.96
0.08 97.81 100.6 117.5 120.3
0.12 106.11 256.52 -41.25 109.17
0.27 73.01 44.12 56.03 27.14
0.02 54.4 53.5 161.07 160.18
0.06 84.99 251.25 -20.93 145.34
0.13 16 75.21 17.74 76.95
0.93 93.51 45.23 98.66 50.37
0.24 43.16 83.07 62.68 102.59
-0.02 26.07 72.34 12.59 58.86
0.23 21.92 103.13 -11.54 69.67
0.07 82.71 125.28 50.83 93.39
0.14 69.74 325.97 -128.97 127.26
0.09 121.02 85.94 106.96 71.88
0.31 38.12 156.71 19.86 138.45
-0.08 114.95 170.1 31.33 86.48
-0.24 50 71.43 146.53 167.96
0.07 71.67 81.61 76.1 86.04
0.13 46.92 80.81 170.12 204.01
0.25 14.9 57.45 18.07 60.62
0.33 29.88 123.7 40.65 134.47
0.07 109.36 89.53 126.51 106.68
0.23 61.57 123.69 34.75 96.88
0.23 21.92 103.13 -11.54 69.67
0.09 39.95 158.91 -2.68 116.28
0.13 46.92 80.81 170.12 204.01
1.14 63.07 70.77 92.23 99.94
0.12 174.39 167.15 120.19 112.96
0.07 71.67 81.61 76.1 86.04
0.04 34.61 76.22 30.95 72.56
0.06 107.65 96.62 66.34 55.31
-0.02 26.07 72.34 12.59 58.86
0.06 84.99 251.25 -20.93 145.34
0.19 101 108.67 26.81 34.48
0.14 69.74 325.97 -128.97 127.26
0.06 110.24 75.29 128.64 93.7
0.33 29.88 123.7 40.65 134.47
0.11 53.05 69.48 126.67 143.1
0.08 97.81 100.6 117.5 120.3
0.29 57.29 59.04 31.91 33.66
0.23 61.57 123.69 34.75 96.88
0.12 77.76 87.72 22.67 32.63
0.17 16.67 81.82 8.89 74.04
0.27 73.01 44.12 56.03 27.14
0.11 89.28 71.45 59.87 42.04
0.12 102.96 204.86 22.75 124.65
0.09 0 0 0 0
0.07 51.72 37.36 129.45 115.08
0.07 61.77 114.19 95.68 148.1
0.24 43.16 83.07 62.68 102.59
0.12 106.11 256.52 -41.25 109.17
0.12 94.98 332.26 -112.52 124.76
-0.24 50 71.43 146.53 167.96
0.07 82.71 125.28 50.83 93.39
0.3 24.33 75.86 -43.2 8.34
0.02 54.4 53.5 161.07 160.18
0.15 126.82 79.99 204.05 157.23
0.16 30.76 78.61 4.15 52
0.27 90.21 71.34 87 68.13
0.38 50.22 108.38 37.44 95.61
0.93 93.51 45.23 98.66 50.37
-0.08 114.95 170.1 31.33 86.48
0.25 14.9 57.45 18.07 60.62
0.09 121.02 85.94 106.96 71.88
0.31 38.12 156.71 19.86 138.45
0.13 16 75.21 17.74 76.95
0.07 109.36 89.53 126.51 106.68
0.38 50.22 108.38 37.44 95.61
0.12 102.96 204.86 22.75 124.65
0.24 43.16 83.07 62.68 102.59
0.04 34.61 76.22 30.95 72.56
0.29 57.29 59.04 31.91 33.66
0.11 53.05 69.48 126.67 143.1
0.06 84.99 251.25 -20.93 145.34
0.12 174.39 167.15 120.19 112.96
0.25 14.9 57.45 18.07 60.62
1.14 63.07 70.77 92.23 99.94
0.3 24.33 75.86 -43.2 8.34
0.16 30.76 78.61 4.15 52
0.27 73.01 44.12 56.03 27.14
0.13 16 75.21 17.74 76.95
0.27 90.21 71.34 87 68.13
-0.24 50 71.43 146.53 167.96
0.07 61.77 114.19 95.68 148.1
0.12 94.98 332.26 -112.52 124.76
0.07 71.67 81.61 76.1 86.04
0.11 89.28 71.45 59.87 42.04
0.14 69.74 325.97 -128.97 127.26
0.09 0 0 0 0
0.31 38.12 156.71 19.86 138.45
0.06 110.24 75.29 128.64 93.7
0.93 93.51 45.23 98.66 50.37
-0.08 114.95 170.1 31.33 86.48
0.09 121.02 85.94 106.96 71.88
-0.02 26.07 72.34 12.59 58.86
0.12 106.11 256.52 -41.25 109.17
0.07 71.67 81.61 76.1 86.04
0.93 93.51 45.23 98.66 50.37
0.12 102.96 204.86 22.75 124.65
0.31 38.12 156.71 19.86 138.45
0.23 21.92 103.13 -11.54 69.67
0.02 54.4 53.5 161.07 160.18
0.07 61.77 114.19 95.68 148.1
0.07 51.72 37.36 129.45 115.08

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